Hallucinations is a big shadow hanging over the rapidly evolving multimodal large language models(MLLMs), referring to that the generated text is inconsistent with the image content. To mitigate hallucinations, existi...
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Hallucinations is a big shadow hanging over the rapidly evolving multimodal large language models(MLLMs), referring to that the generated text is inconsistent with the image content. To mitigate hallucinations, existing studies mainly resort to an instruction-tuning manner that requires retraining the models with specific data. In this paper, we pave a different way, introducing a training-free method named Woodpecker. Like woodpeckers heal trees, it picks out and corrects hallucinations from the generated text. Concretely, Woodpecker consists of five stages: key concept extraction, question formulation, visual knowledge validation, visual claim generation, and hallucination correction. Implemented in a post-remedy manner, Woodpecker can easily serve different MLLMs, while being interpretable by accessing intermediate outputs of the five stages. We evaluate Woodpecker both quantitatively and qualitatively and show the huge potential of this new paradigm. On the POPE benchmark, our method obtains a 30.66%/24.33% improvement in accuracy over the baseline MiniGPT-4/mPLUG-Owl. The source code is released at https://***/BradyFU/Woodpecker.
Graph processing has been widely used in many scenarios,from scientific computing to artificial *** processing exhibits irregular computational parallelism and random memory accesses,unlike traditional ***,running gra...
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Graph processing has been widely used in many scenarios,from scientific computing to artificial *** processing exhibits irregular computational parallelism and random memory accesses,unlike traditional ***,running graph processing workloads on conventional architectures(e.g.,CPUs and GPUs)often shows a significantly low compute-memory ratio with few performance benefits,which can be,in many cases,even slower than a specialized single-thread graph *** domain-specific hardware designs are essential for graph processing,it is still challenging to transform the hardware capability to performance boost without coupled software *** article presents a graph processing ecosystem from hardware to *** start by introducing a series of hardware accelerators as the foundation of this ***,the codesigned parallel graph systems and their distributed techniques are presented to support graph ***,we introduce our efforts on novel graph applications and hardware *** results show that various graph applications can be efficiently accelerated in this graph processing ecosystem.
In an ever-changing environment,Software as a Service(SaaS)can rarely protect users'*** able to manage and control the privacy is therefore an important goal for *** the participant of composite service is substit...
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In an ever-changing environment,Software as a Service(SaaS)can rarely protect users'*** able to manage and control the privacy is therefore an important goal for *** the participant of composite service is substituted,it is unclear whether the composite service satisfy user privacy requirement or *** this paper,we propose a privacy policies automatic update method to enhance user privacy when a service participant change in the composite ***,we model the privacy policies and service variation ***,according to the service variation rules,the privacy policies are automatically generated through the negotiation between user and service ***,we prove the feasibility and applicability of our method with the *** the service quantity is 50,ratio that the services variations are successfully checked by monitor is 81%.Moreover,ratio that the privacy policies are correctly updated is 93.6%.
he advance in Non-Volatile Memory(NVM)has changed the traditional *** to DRAM,NVM has the advantages of nonvolatility and large ***,as the read/write speed of NVM is still lower than that of DRAM,building DRAM/NVM-bas...
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he advance in Non-Volatile Memory(NVM)has changed the traditional *** to DRAM,NVM has the advantages of nonvolatility and large ***,as the read/write speed of NVM is still lower than that of DRAM,building DRAM/NVM-based hybrid memory systems is a feasible way of adding NVM into the current computer *** paper aims to optimize the well-known B^(+)-tree for hybrid *** novelty of this study is ***,we observed that the space utilization of internal nodes in B^(+)-tree is generally below 70%.Inspired by this observation,we propose to maintain hot keys in the free space within internal nodes,yielding a new index named HATree(Hotness-Aware Tree).The new idea of HATree is to use the unused space of the parent of leaf nodes(PLNs)as the hotspot data ***,no extra space is needed,and the in-node hotspot cache can efficiently improve query ***,to further improve the update performance of HATree,we propose to utilize the eADR technology supported by the third-generation Intel Xeon Scalable Processors to enhance HATree with instant log persistence,which results in the new HATree-Log *** conduct extensive experiments on real hybrid memory architecture involving DRAM and Intel Optane Persistent Memory to evaluate the performance of HATree and *** state-of-the-art indices for hybrid memory,namely NBTree,LBTree,and FPTree,are included in the experiments,and the results suggest the efficiency of HATree and HATree-Log.
Industrial cyber-physical systems closely integrate physical processes with cyberspace, enabling real-time exchange of various information about system dynamics, sensor outputs, and control decisions. The connection b...
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Industrial cyber-physical systems closely integrate physical processes with cyberspace, enabling real-time exchange of various information about system dynamics, sensor outputs, and control decisions. The connection between cyberspace and physical processes results in the exposure of industrial production information to unprecedented security risks. It is imperative to develop suitable strategies to ensure cyber security while meeting basic performance *** the perspective of control engineering, this review presents the most up-to-date results for privacy-preserving filtering,control, and optimization in industrial cyber-physical systems. Fashionable privacy-preserving strategies and mainstream evaluation metrics are first presented in a systematic manner for performance evaluation and engineering *** discussion discloses the impact of typical filtering algorithms on filtering performance, specifically for privacy-preserving Kalman filtering. Then, the latest development of industrial control is systematically investigated from consensus control of multi-agent systems, platoon control of autonomous vehicles as well as hierarchical control of power systems. The focus thereafter is on the latest privacy-preserving optimization algorithms in the framework of consensus and their applications in distributed economic dispatch issues and energy management of networked power systems. In the end, several topics for potential future research are highlighted.
Transition metal molybdate(RMoO4)has been extensively studied in optics,electricity and catalysis due to its low cost and multivalent Mo atoms,while its poor electrical conductivity limits its application in electroma...
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Transition metal molybdate(RMoO4)has been extensively studied in optics,electricity and catalysis due to its low cost and multivalent Mo atoms,while its poor electrical conductivity limits its application in electromagnetic wave absorption(EMA).Compounding molybdate with conductive materials is an effective way to improve its electrical *** this work,a homogeneous coating on the molybdate surface was achieved by utilizing a pyrrole oxidation polymerization mechanism,and then stabilized composites of Molybdate@Carbon(RMoO4@C)were obtained by high-temperature *** EMA properties of the nanocomposites were tuned by substituting the species of transition metal elements(RMoO_(4)@C,R=Fe,Co,Cu).Among them,Fe_(2)(MoO_(4))_(3)@C has a minimum reflection loss of−60.62 dB at an ultra-low thickness of 1.7 mm,and the effective absorption bandwidth(EAB)can reach 4.75 GHz at 3.00 *** work expands the range of applications for molybdate and provides a new material basis for building high-performance EMA materials.
Software-defined networking decouples the control plane from the data plane to enable centralized flow-level network management, while requiring periodically collecting traffic statistics from the data plane to enforc...
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Software-defined networking decouples the control plane from the data plane to enable centralized flow-level network management, while requiring periodically collecting traffic statistics from the data plane to enforce optimal management. As one of the most important traffic measurement tasks, heavy flow detection has received wide attention for its providing fundamental statistics in various practical applications. Existing studies have proposed sketch-based detection solutions to address the mismatch problem between massive traffic and limited high-speed memory resources for measurement in the data plane. However,they overlook the potential of integrating the flow table, where each entry simultaneously enforces forwarding rules for specific flows and records flow statistics into the sketch design, leading to redundant measurement between the flow table and sketch and being unable to utilize their statistics to jointly enhance estimation accuracy. We propose flow entries assisted sketch(FEA-Sketch) in this work, which employs a differentiated flow recording strategy to record flow statistics jointly using the flow table and sketch for memory-efficient and computationally efficient heavy flow detection. We also propose an optimization-based estimation algorithm to accurately recover per-flow sizes for the flows that only have aggregated statistics due to the sharing of entries in the table(or counters in the sketch). We extend the FEA-Sketch to the distributed measurement setting with a hop-based collaborative measurement strategy, which reduces the measurement workload on switches across the network by avoiding redundant measurements. The experimental results on real Internet traces show that the accuracy of heavy flow detection is improved up to 1.95 times, and the bias of flow size estimation is improved up to 2.99 times, demonstrating that integrating flow entries can significantly improve the performance of heavy flow detection.
Rapid urbanization has made road construction and maintenance imperative, but detecting road diseases has been time-consuming with limited accuracy. To overcome these challenges, we propose an efficient YOLOv7 road di...
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This paper examines fault-tolerant quantized control for neural networks under persistent dwell-time switching, considering the presence of actuator faults and dynamic output quantization. The dynamic scaling factor (...
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Dear Editor,This letter presents a distributed adaptive second-order latent factor(DAS) model for addressing the issue of high-dimensional and incomplete data representation. Compared with first-order optimizers, a se...
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Dear Editor,This letter presents a distributed adaptive second-order latent factor(DAS) model for addressing the issue of high-dimensional and incomplete data representation. Compared with first-order optimizers, a second-order optimizer has stronger ability in approaching a better solution when dealing with the non-convex optimization problems, thus obtaining better performance in extracting the latent factors(LFs) well representing the known information from high-dimensional and incomplete data.
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